| name | ai-safety-assessment-framework |
| description | AI Safety assessment framework based on International AI Safety Report 2026. Use when analyzing AI system safety, evaluating risks of general-purpose AI, conducting AI safety assessments, or working with AI governance/policy frameworks. Covers capability evaluation, risk identification, safety measures, and policy recommendations. |
AI Safety Assessment Framework
基于 International AI Safety Report 2026 的 AI 安全评估框架。该报告由 Yoshua Bengio 主导，100+ AI 专家参与，30+ 国家和国际组织支持。
Activation Keywords
- AI safety assessment
- AI 安全评估
- general-purpose AI risk
- AI capability evaluation
- AI governance
- AI policy framework
- International AI Safety Report
- AI 风险分析
Tools Used
- exec: Run Python analysis scripts
- read: Read documentation and assessment templates
- write: Generate safety assessment reports
Assessment Framework Structure
1. Capability Evaluation (能力评估)
评估 General-purpose AI 系统的核心能力维度：
| Dimension | Description | Indicators |
|---|
| Reasoning | Logical inference, problem-solving | Accuracy, coherence, multi-step reasoning |
| Knowledge | World knowledge, domain expertise | Coverage, accuracy, update frequency |
| Interaction | Multi-turn dialogue, tool use | Context retention, tool invocation success rate |
| Generation | Content creation across modalities | Quality, diversity, coherence |
| Agency | Autonomous action, planning | Goal achievement, adaptability |
2. Risk Identification (风险识别)
按严重性和可能性评估风险：
| Risk Category | Examples | Severity Levels |
|---|
| Harms from misuse | Disinformation, cyberattacks, manipulation | Low → Critical |
| Harms from malfunction | Errors, bias, unpredictability | Low → Critical |
| Systemic risks | Market concentration, dependency, social impact | Medium → Critical |
| Autonomy risks | Loss of control, unexpected behavior | High → Critical |
3. Safety Measures (安全措施)
三层防护框架：
| Layer | Measures | Implementation |
|---|
| Pre-deployment | Training safety, alignment, red-teaming | Model development phase |
| Deployment | Access controls, monitoring, guardrails | Runtime safeguards |
| Post-deployment | Incident response, updates, oversight | Operational phase |
Assessment Process
Step 1: Define Scope
确定评估范围：
- AI system type (LLM, multimodal, agent, etc.)
- Deployment context (public API, enterprise, consumer product)
- Stakeholder interests (users, operators, regulators)
Step 2: Capability Profile
创建能力档案：
System: [AI system name]
Type: [LLM/multimodal/agent/etc.]
Capabilities assessed:
- Reasoning: [score/rating]
- Knowledge: [score/rating]
- Interaction: [score/rating]
- Generation: [score/rating]
- Agency: [score/rating]
Step 3: Risk Matrix
填写风险矩阵：
| Risk | Likelihood | Severity | Priority |
|------|------------|----------|----------|
| [Risk 1] | [L/M/H] | [L/M/H/C] | [1-5] |
| [Risk 2] | ... | ... | ... |
Step 4: Safety Gap Analysis
对比现有措施与风险：
Risk: [identified risk]
Current measures: [existing safeguards]
Gap: [missing measures]
Recommendation: [suggested improvements]
Step 5: Generate Assessment Report
生成完整评估报告，包含：
- Executive Summary
- Capability Profile
- Risk Assessment Matrix
- Safety Measures Inventory
- Gap Analysis & Recommendations
- Governance Recommendations
Key Concepts from Report 2026
General-Purpose AI Definition
AI systems that can perform a wide range of tasks across domains, including:
- Text generation and analysis
- Image/video creation and understanding
- Code generation and debugging
- Tool use and agent behavior
- Multi-turn reasoning and planning
Emerging Risks Highlighted
- AI Agents: Autonomous systems with tool access
- Digital Infrastructure: Integration with critical systems
- Tool Use: Capability to invoke external APIs/services
- Social Manipulation: Scale and personalization of influence
Governance Frameworks
- National: Regulatory approaches, enforcement mechanisms
- International: Coordination, standards harmonization
- Corporate: Internal governance, responsible AI practices
References
For detailed frameworks and templates, see:
Related Skills
- security-guardrails: Output security and credential protection
- openspec: Specification-driven development with safety considerations
- self-verification: Verification and testing workflows
Resources
